Effective diagnosis of diabetes with a decision tree-initialised neuro-fuzzy approach

Tianhua Chen, Changjing Shang, Pan Su, Grigoris Antoniou, Qiang Shen

Allbwn ymchwil: Cyfraniad at gynhadleddPapuradolygiad gan gymheiriaid

7 Dyfyniadau (Scopus)

Crynodeb

Diabetes mellitus is a serious hazard to human health that can result in a number of severe complications. Early diagnosis and treatment is of significant importance to patients for the acquisition of a better quality life and precaution against subsequent complications. This paper proposes an approach by learning a fuzzy rule base for the effective diagnosis of diabetes mellitus. In particular, the proposed approach starts with the generation of a crisp rule base through a decision tree learning mechanism, which is data-driven and able to learn simple rule structures. The crisp rule base is then transformed into a fuzzy rule base, which forms the input to the powerful neuro-fuzzy framework of ANFIS, further optimising the parameters of both rule antecedents and consequents. Experimental study on the well-known Pima Indian diabetes data set is provided to demonstrate the promising potential of the proposed approach
Iaith wreiddiolSaesneg
Tudalennau227-239
Nifer y tudalennau13
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 11 Awst 2018
DigwyddiadUKCI 2018: Advances in Intelligent Systems and Computing - Nottingham, Teyrnas Unedig Prydain Fawr a Gogledd Iwerddon
Hyd: 05 Medi 201807 Medi 2018

Cynhadledd

CynhadleddUKCI 2018
Gwlad/TiriogaethTeyrnas Unedig Prydain Fawr a Gogledd Iwerddon
DinasNottingham
Cyfnod05 Medi 201807 Medi 2018

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